The Strategic Guide to Responsible Platform Business highlights alternative strategies that platform organisations can pursue to acknowledge and mitigate platform capitalism’s problematic tendencies. While it is informative for policymakers and civil society, the Guide is mainly meant to be a resource for decision-makers in European platform firms. It outlines key ethical concerns and trade-offs, to then point to possible responses that have been tried by platforms operating in the market.

China has revealed its first set of ethical guidelines governing artificial intelligence, placing emphasis on protecting user rights and preventing risks in ways that align with Beijing's goals of reining in Big Tech's influence and becoming the global AI leader by 2030.

This session will share the results and learnings of the creation and development of an ethical “yardstick” for respectful technology, including its application to websites and mobile apps. The speakers will also explore learnings from everyday people in the validation research around the certification mark as well as share recommendations for tech makers.

The research reveals some of the broad shapes and patterns of the tech and data ecosystem and its relationships to current ethics tools. It covers three key sections:

• Early tech and data ethics tools and their users
• The evolving tech and data ethics landscape
• Lessons for the next generation of tech and data ethics tools

The tech giants have developed a new vocabulary to use when they want to assure the public that they care deeply about developing AI responsibly—but want to make sure they don’t invite too much scrutiny. Here’s an insider’s guide to decoding their language and challenging the assumptions and values baked in.

The State of AI Ethics Report (January 2021) captures the most relevant developments in AI Ethics since October of 2020.

The growth of research projects relying on pervasive data — big datasets about people’s lives and activities that can be collected without them knowing — are testing the ethical frameworks and assumptions traditionally used by researchers and ethical review boards to ensure adequate protection of human subjects.

On December 2, after a protracted disagreement over the release of a research paper, Google forced out its ethical AI co-lead, Timnit Gebru. The paper was on the risks of large language models, AI models trained on staggering amounts of text data, which are a line of research core to Google’s business. Gebru, a leading voice in AI ethics, was one of the only Black women at Google Research.

A boom in contact tracing devices could herald a new era of worker surveillance.

It is not uncommon now for AI experts to ask whether an AI is ‘fair’ and ‘for good’. But ‘fair’ and ‘good’ are infinitely spacious words that any AI system can be squeezed into. The question to pose is a deeper one: how is AI shifting power?

今日、AIの専門家がAIが「フェア/公正」で「グッド/良い」かどうかを尋ねることは珍しくありません。どんなAIシステムにも適用できる質問です。しかし、筆者が提起する問題はより深いものです。AIを用いて、誰がどのようにして新たな権力を握っているのでしょうか。

ネットいじめは、今日のデジタル社会におけるオンライン社会的相互作用の中で広く見られる有害な現象です。多くの計算科学は機械学習アルゴリズムを用いたネットいじめの検出パフォーマンスの向上に焦点を当てていますが、そこで提案されたモデルは、意図しない社会的偏見をもたらす傾向にあります。この研究では、「公平性の制約を備えたトレーニングによってモデルを導出することで、ネットいじめ検出モデルの意図しないバイアスを軽減できるか」という調査質問に答えようとします。

Cyberbullying is a widespread adverse phenomenon among online social interactions in today's digital society. While numerous computational studies focus on enhancing the cyberbullying detection performance of machine learning algorithms, proposed models tend to carry and reinforce unintended social biases. In this study, we try to answer the research question of "Can we mitigate the unintended bias of cyberbullying detection models by guiding the model training with fairness constraints?".

How should democracies effectively compete against authoritarian regimes in the AI space? This report offers a “terrain strategy” for the United States to leverage the malleability of artificial intelligence to offset authoritarians' structural advantages in engineering and deploying AI.

This paper discusses the establishment of a governance framework to secure the development and deployment of “good AI”, and describes the quest for a morally objective compass to steer it.

IBM and Microsoft are among the first to sign the Vatican’s “Rome Call for AI Ethics.”

Ruoss et al. published the first method to train AI systems with mathematically provable certificates of individual fairness. Full source code is available on Github.

Companies discover data ethics as part of their corporate communications: IKEA promises to embed data ethics into all their processes.

The not-so-secret ingredient that underlies all successful Artificial Intelligence / Machine Learning (AI/ML) methods is training data. There would be no facial recognition, no targeted advertisements and no self-driving cars if it was not for large enough data sets with which those algorithms have been trained to perform their tasks. Given how central these data sets are, important ethics questions arise: How is data collection performed? And how do we govern its' use? This chapter – part of a forthcoming book – looks at why new data governance strategies are needed; investigates the relation of different data governance models to historic consent approaches; and compares different implementations of personal data exchange models.

A Silicon Valley lobby enrolled elite academia to avoid legal restrictions on artificial intelligence.

Interesting article using intersectional analysis do define and recommend mitigations to emergent discrimination in algorithmic systems.

My colleague Camille Nebeker at UCSD specializes in ethics with digital health – she's developed a checklist for “safe and responsible digital health research” - it's private but you can request a copy.

The United States Department of Defense (DoD) enduring challenge is to retain a technological and military advantage while upholding and promoting democratic values, working with our allies, and contributing to a stable, peaceful international community. DoD’s development and use of artificial intelligence (AI) reflects this challenge.
The Defense Innovation Board (DIB) recommends five AI ethics principles for adoption by DoD, which in shorthand are: responsible, equitable, traceable, reliable, and governable. These principles and a set of recommended actions in support of them are described in this document.

Earlier this week, I traveled to Paris to attend the Global Forum on Artificial Intelligence for Humanity (GFIAH). The by-invitation event featured one day of workshops addressing issues such as AI and culture, followed by a two days of panels on developing trustworthy AI, data governance, the future of work, delegating decisions to machines, bias and AI, and future challenges. The event was a part of the French government's effort to take the lead on developing a new AI regulatory framework that it describes as a 'third way', distinct from the approach to AI in China and the United States.

You may have seen the EU Guidelines on Ethics in AI. As I believe we are entering in a 'regulating AI debate', it's worth sharing it here.

Take the Tech Pledge and join the movement: Make tech a force for good. Created by 150 people in technology during this years Techfestival.

AI is increasingly being embedded in our lives, supplementing our pervasive use of digital technologies. But this is being accompanied by disquiet over problematic and dangerous implementations of AI, or indeed, even AI itself deciding to do dangerous and problematic actions, especially in fields such as the military, medicine and criminal justice.

A gaping ethical dilemma at the very heart of the data protection ecosystem practically guarantees widespread privacy wrongs.